CN109063676A - A kind of adaptive time-frequency method method and system for power signal - Google Patents
A kind of adaptive time-frequency method method and system for power signal Download PDFInfo
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Abstract
The invention discloses a kind of adaptive time-frequency method method and system for power signal.The filtering method includes: to obtain power signal sequence；The time window length of each signal in power signal sequence is determined according to power signal sequence；The power signal sequence is handled point by point, building parsing sequence；Determine pseudo NM-algebra of the parsing sequence under the different time window length；Determine that power signal restores sequence according to the pseudo NM-algebra；Restore the distribution that sequence determines pure power signal according to the power signal；The pure power signal is muting power signal；Best Times length of window is determined according to the distribution；The power signal sequence is filtered according to the Best Times length of window, determines filtered power signal sequence.Filter effect to the non-stationary non-Gaussian noise in power signal sequence can be enhanced using filtering method provided by the present invention and system.
Description
Technical field
The present invention relates to power domains, more particularly to a kind of adaptive time-frequency method method for power signal
And system.
Background technique
With the development of smart grid, the analysis of household electricity load is become more and more important.Pass through point of power load
Analysis, domestic consumer can obtain the power information of each electric appliance and the fining inventory of the electricity charge in time；Power department can obtain
More detailed user power utilization information is obtained, and the accuracy of electro-load forecast can be improved, provides overall planning for power department
Foundation, meanwhile, using the power information of each electric appliance, would know that the electricity consumption behavior of user, this for family's energy consumption assessment and
The research of Energy Saving Strategy has directive significance.
Load switch event detection is a step mostly important in Energy Decomposition, should detect event, simultaneously also
Can determine that event occur at the time of, but switch events detection precision by noise in power signal (power sequence) influenced compared with
Greatly, the application especially with non-linear electric appliance and universal, ambient noise shows more apparent non-stationary and non-gaussian
Characteristic further affects detection accuracy；It therefore, is a very important step in switch events detection to power signal filtering；
And common low-pass filter and median filter are difficult to reach ideal filtering effect in non-stationary and non-Gaussian noise environment
Fruit.
Summary of the invention
The object of the present invention is to provide a kind of adaptive time-frequency method method and system for power signal, with solution
Certainly conventional filter is difficult to the problem of reaching ideal filter effect in non-stationary and non-Gaussian noise environment.
To achieve the above object, the present invention provides following schemes:
A kind of adaptive time-frequency method method for power signal, comprising:
Obtain power signal sequence；
The time window length of each signal in the power signal sequence is determined according to the power signal sequence；
The power signal sequence is handled point by point, building parsing sequence；
Determine pseudo NM-algebra of the parsing sequence under the different time window length；
Determine that power signal restores sequence according to the pseudo NM-algebra；
Restore the distribution that sequence determines pure power signal according to the power signal；The pure power signal is that nothing is made an uproar
The power signal of sound；
Best Times length of window is determined according to the distribution；
The power signal sequence is filtered according to the Best Times length of window, determines filtered power letter
Number sequence.
Optionally, the time that each signal in the power signal sequence is determined according to the power signal sequence
Length of window specifically includes:
Dominant frequency is determined according to the power signal sequence；
Obtain sample frequency；
Time window foundation length is calculated according to the dominant frequency and the sample frequency；
Determine that the time window of each signal in the power signal sequence is long according to the time window foundation length
Degree.
Optionally, described to be handled point by point the power signal sequence, building parsing sequence specifically includes:
According to formulaBuilding parsing sequence；Wherein, z (n) is the nth point of constructed parsing sequence
Value；μ is the index of modulation, 1 μ≤2 <；I is imaginary unit,PjFor j-th of signal in power signal sequence P.
Optionally, pseudo- Wigner-Ville of the determination parsing sequence under the different time window length
Distribution, specifically includes:
According to formulaDetermine that the parsing sequence is worked as described
Pseudo NM-algebra under front layer time window length；Wherein, PWz c(n, f) is the puppet under c-th of time window length
Wigner-Ville distribution；hcIt (m) is the time window function under c-th of time window length；z*It (n-m) is the conjugation of z (n-m),
Since z (n-m) is real number, z*(n-m)=z (n-m), z (n+m) are the value of the n-th+m elements of the parsing sequence, e-i4πfm
=cos (4 π fm)-isin (4 π fm), i is imaginary unit, and n is serial number, is positive integer；M is the parameter in sum term, is integer；
F is frequency.
Optionally, described to determine that power signal restores sequence according to the pseudo NM-algebra, it specifically includes:
According to formulaDetermine that power signal restores sequence；Wherein,For power
Signal restores sequence.
Optionally, described to restore the distribution that sequence determines pure power signal according to the power signal, it specifically includes:
According to formula Dc(n)=[Lc(n),Uc(n)] distribution of pure power signal is determined；Wherein,LcIt (n) is Dc(n) lower bound；Uc(n)
For Dc(n) the upper bound；For
Mean square deviation；PnFor the power signal sequence of actual measurement；σvFor power
The mean square deviation of noise in signal sequence；median(|Pn-Pn-1|, n=2 ..., N) be power signal sequence | Pn-Pn-1| intermediate value.
A kind of adaptive time-frequency method system for power signal, comprising:
Power signal retrieval module, for obtaining power signal sequence；
Time window length determination modul, it is every in the power signal sequence for being determined according to the power signal sequence
The time window length of one signal；
Sequence construct module is parsed, for being handled point by point the power signal sequence, building parsing sequence；
Pseudo NM-algebra determining module, for determining that the parsing sequence is long in the different time windows
Pseudo NM-algebra under degree；
Power signal restores sequence determining module, for determining that power signal is extensive according to the pseudo NM-algebra
Complex sequences；
Distribution determining module, for restoring the distribution model that sequence determines pure power signal according to the power signal
It encloses；The pure power signal is muting power signal；
Best Times length of window determining module, for determining Best Times length of window according to the distribution；
Filter module is determined for being filtered according to the Best Times length of window to the power signal sequence
Filtered power signal sequence.
Optionally, the time window length determination modul specifically includes:
Dominant frequency determination unit, for determining dominant frequency according to the power signal sequence；
Sample frequency acquiring unit, for obtaining sample frequency；
Time window foundation length calculates unit, for calculating time window according to the dominant frequency and the sample frequency
Foundation length；
Time window length determination unit, for determining the power signal sequence according to the time window foundation length
The time window length of each interior signal.
Optionally, the parsing sequence construct module specifically includes:
Sequence construct unit is parsed, for according to formulaBuilding parsing sequence；Wherein, z (n) is institute's structure
The value of the nth point for the parsing sequence built；μ is the index of modulation, 1 μ≤2 <；I is imaginary unit,PjFor power signal sequence
Arrange j-th of signal in P.
Optionally, the pseudo NM-algebra determining module specifically includes:
Pseudo NM-algebra determination unit, for according to formula
Determine pseudo NM-algebra of the parsing sequence under the current layer time window length；Wherein, PWz c(n,f)
For the pseudo NM-algebra under c-th of time window length；hcIt (m) is the time window under c-th of time window length
Function；z*It (n-m) is the conjugation of z (n-m), since z (n-m) is real number, z*(n-m)=z (n-m), z (n+m) are the parsing sequence
The value of n-th+m elements of column, e-i4πfm=cos (4 π fm)-isin (4 π fm), i is imaginary unit, and n is serial number, is positive integer；
M is the parameter in sum term, is integer；F is frequency.
The specific embodiment provided according to the present invention, the invention discloses following technical effects: the present invention provides one kind
For the adaptive time-frequency method method and system of power signal, traffic filter of the practical application in electric system, benefit
The non-stationary of signal is overcome with pseudo- Wigner-Ville time-frequency distributions, it is therefore, non-for the non-stationary in power signal sequence
Gaussian noise has stronger filter effect.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is the adaptive time-frequency method method flow diagram provided by the present invention for power signal；
Fig. 2 is adaptive Time-Frequency Domain Filtering algorithm flow chart provided by the present invention；
Fig. 3 is the schematic diagram provided by the present invention about window；
Fig. 4 is the adaptive time-frequency method system construction drawing provided by the present invention for power signal.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The object of the present invention is to provide a kind of adaptive time-frequency method method and system for power signal, can
Enhance the filter effect to the non-stationary non-Gaussian noise in power signal sequence.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Applying mode, the present invention is described in further detail.
Fig. 1 is the adaptive time-frequency method method flow diagram provided by the present invention for power signal, such as Fig. 1 institute
Show, a kind of adaptive time-frequency method method for power signal, comprising:
Step 101: obtaining power signal sequence.
Fig. 2 is adaptive Time-Frequency Domain Filtering algorithm flow chart provided by the present invention, as shown in Fig. 2, input power signal
Sequence P=[P1,P2,…,PN], N is the length of signal sequence.
Step 102: the time window of each signal in the power signal sequence is determined according to the power signal sequence
Mouth length.
The power spectrum of list entries is calculated, and dominant frequency f is found out according to power spectrumd；Calculate the foundation length W of time windowL:Wherein fsFor sample frequency；Seek the different length l of time windowi:lc=(c-1) WL, c=1,2 ..., 200,
The parameter that 200 length is used to use when puppet Wigner-Ville time-frequency distributions.
Step 103: the power signal sequence being handled point by point, building parsing sequence.
Power data is handled point by point, it is assumed that currently processed data point serial number n constructs the analytic value z of nth point
(n):Wherein, μ is the index of modulation, 1 μ≤2 <, can any value according to the actual situation, not no essential shadow
It rings.
Step 104: determining pseudo- Wigner-Ville points of the parsing sequence under the different time window length
Cloth.
Calculate pseudo NM-algebra PW of parsing sequence z (n) under different time window lengthz c(n, f):C=1,2 ..., 200, wherein hcIt (m) is time window letter
Number, length of window lc；The generally rectangular cross-section window of time window type or Hamming window, no substantial influence can be according to practical feelings
Condition selection.
Step 105: determining that power signal restores sequence according to the pseudo NM-algebra.
According to formulaDetermine that power signal restores sequence；Wherein,For power
Signal restores sequence.
Step 106: the distribution that sequence determines pure power signal is restored according to the power signal；The net work rate letter
Number be muting power signal.
According to formula Dc(n)=[Lc(n),Uc(n)] distribution of pure power signal is determined；Wherein,LcIt (n) is Dc(n) lower bound；Uc(n)
For Dc(n) the upper bound；For
Mean square deviation；PnFor the power signal sequence of actual measurement；σvFor power
The mean square deviation of noise in signal sequence；median(|Pn-Pn-1|, n=2 ..., N) be power signal sequence | Pn-Pn-1| intermediate value.
Step 107: Best Times length of window is determined according to the distribution.
Choose optimal time window length lOPT:
For serial number n at the same time and different length of window lc, hc(n) there is different distribution Dc(n), exist
Window serial number i1,i2,…,iKMeet:
(1)i1< i2< ... < iK；
(2)Dc(n)∩Dc+1(n)=Φ: c={ i1,i2,…,iK, Φ indicates empty set.Fig. 3 is pass provided by the present invention
In the schematic diagram of window, as shown in figure 3, condition (2) indicates the time window of adjacent serial number, corresponding distribution Dc(n)
And Dc+1(n) without duplicate part.
The two conditions indicate: two adjacent length of window, the unfolded part of corresponding distribution.
Optimal time window length lOPT: in this K length of window, select maximum value lOPT=max [lc(n), c=
i1,i2,…,iK]。
Step 108: the power signal sequence being filtered according to the Best Times length of window, after determining filtering
Power signal sequence.
Fig. 4 is the adaptive time-frequency method system construction drawing provided by the present invention for power signal, such as Fig. 4 institute
Show, a kind of adaptive time-frequency method system for power signal, comprising:
Power signal retrieval module 401, for obtaining power signal sequence.
Time window length determination modul 402, for determining the power signal sequence according to the power signal sequence
The time window length of each interior signal.
The time window length determination modul 402 specifically includes: dominant frequency determination unit, for according to the power signal
Sequence determines dominant frequency；Sample frequency acquiring unit, for obtaining sample frequency；Time window foundation length calculates unit, is used for
Time window foundation length is calculated according to the dominant frequency and the sample frequency；Time window length determination unit is used for root
The time window length of each signal in the power signal sequence is determined according to the time window foundation length.
Sequence construct module 403 is parsed, for being handled point by point the power signal sequence, building parsing sequence.
The parsing sequence construct module 403 specifically includes: parsing sequence construct unit, for according to formulaBuilding parsing sequence；Wherein, z (n) is the value of the nth point of constructed parsing sequence；μ is the index of modulation, 1
μ≤2 <；I is imaginary unit；PjFor j-th of signal in power signal sequence P.
Pseudo NM-algebra determining module 404, for determining the parsing sequence in the different time windows
Pseudo NM-algebra under mouth length.
The pseudo NM-algebra determining module 404 specifically includes: pseudo NM-algebra determination unit,
For according to formulaDetermine that the parsing sequence is worked as described
Pseudo NM-algebra under front layer time window length；Wherein, PWz c(n, f) is the puppet under c-th of time window length
Wigner-Ville distribution；hcIt (m) is the time window function under c-th of time window length；z*It (n-m) is the conjugation of z (n-m),
Since z (n-m) is real number, z*(n-m)=z (n-m), z (n+m) are the value of the n-th+m elements of the parsing sequence, e-i4πfm
=cos (4 π fm)-isin (4 π fm), i is imaginary unit, and n is serial number, is positive integer；M is the parameter in sum term, is integer；
F is frequency.
Power signal restores sequence determining module 405, for determining that power is believed according to the pseudo NM-algebra
Number restore sequence.
Distribution determining module 406, for restoring the distribution that sequence determines pure power signal according to the power signal
Range；The pure power signal is muting power signal.
Best Times length of window determining module 407, for determining Best Times length of window according to the distribution.
Filter module 408, for being filtered according to the Best Times length of window to the power signal sequence, really
Fixed filtered power signal sequence.
The adaptive time-frequency method method and system for being used for power signal proposed by the invention are used for power train
The traffic filter of system obtains the Time-Frequency Domain Filtering device based on pseudo NM-algebra.Due to being used in this filter
The superposition of noisy data can effectively eliminate stationary noise influence；Pseudo- Wigner- is utilized in filter simultaneously
Ville time-frequency distributions, and this distribution can overcome the non-stationary of signal, therefore for the non-stationary non-gaussian in power sequence
Noise also has stronger filter effect.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.For system disclosed in embodiment
For, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is said referring to method part
It is bright.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said
It is bright to be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, foundation
Thought of the invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not
It is interpreted as limitation of the present invention.
Claims (10)
1. a kind of adaptive time-frequency method method for power signal characterized by comprising
Obtain power signal sequence；
The time window length of each signal in the power signal sequence is determined according to the power signal sequence；
The power signal sequence is handled point by point, building parsing sequence；
Determine pseudo NM-algebra of the parsing sequence under the different time window length；
Determine that power signal restores sequence according to the pseudo NM-algebra；
Restore the distribution that sequence determines pure power signal according to the power signal；The pure power signal is muting
Power signal；
Best Times length of window is determined according to the distribution；
The power signal sequence is filtered according to the Best Times length of window, determines filtered power signal sequence
Column.
2. filtering method according to claim 1, which is characterized in that described according to power signal sequence determination
The time window length of each signal in power signal sequence, specifically includes:
Dominant frequency is determined according to the power signal sequence；
Obtain sample frequency；
Time window foundation length is calculated according to the dominant frequency and the sample frequency；
The time window length of each signal in the power signal sequence is determined according to the time window foundation length.
3. filtering method according to claim 1, which is characterized in that described to be located point by point to the power signal sequence
Reason, building parsing sequence, specifically includes:
According to formulaBuilding parsing sequence；Wherein, z (n) is the value of constructed parsing sequence nth point；μ is
The index of modulation, 1 μ≤2 <；I is imaginary unit,PjFor j-th of signal in power signal sequence P.
4. filtering method according to claim 3, which is characterized in that the determination parsing sequence is described in different
Pseudo NM-algebra under time window length, specifically includes:
According to formulaDetermine the parsing sequence in the current layer
Pseudo NM-algebra under time window length；Wherein, PWz c(n, f) is the puppet under c-th of time window length
Wigner-Ville distribution；hcIt (m) is the time window function under c-th of time window length；z*It (n-m) is the conjugation of z (n-m),
Since z (n-m) is real number, z*(n-m)=z (n-m), z (n+m) are the value of the n-th+m elements of the parsing sequence, e-i4πfm
=cos (4 π fm)-isin (4 π fm), i is imaginary unit, and n is serial number, is positive integer；M is the parameter in sum term, is integer；
F is frequency.
5. filtering method according to claim 4, which is characterized in that described true according to the pseudo NM-algebra
Determine power signal and restore sequence, specifically includes:
According to formulaDetermine that power signal restores sequence；Wherein,For power letter
Number restore sequence.
6. filtering method according to claim 5, which is characterized in that described to be determined according to power signal recovery sequence
The distribution of pure power signal, specifically includes:
According to formula Dc(n)=[Lc(n),Uc(n)] distribution of pure power signal is determined；Wherein,LcIt (n) is Dc(n) lower bound；Uc(n)
For Dc(n) the upper bound； For
Mean square deviation；PnFor the power signal sequence of actual measurement；σvFor power
The mean square deviation of noise in signal sequence；median(|Pn-Pn-1|, n=2 ..., N) be power signal sequence | Pn-Pn-1| intermediate value.
7. a kind of adaptive time-frequency method system for power signal characterized by comprising
Power signal retrieval module, for obtaining power signal sequence；
Time window length determination modul, for determining each in the power signal sequence according to the power signal sequence
The time window length of signal；
Sequence construct module is parsed, for being handled point by point the power signal sequence, building parsing sequence；
Pseudo NM-algebra determining module, for determining the parsing sequence under the different time window length
Pseudo NM-algebra；
Power signal restores sequence determining module, for determining that power signal restores sequence according to the pseudo NM-algebra
Column；
Distribution determining module, for restoring the distribution that sequence determines pure power signal according to the power signal；Institute
Stating pure power signal is muting power signal；
Best Times length of window determining module, for determining Best Times length of window according to the distribution；
Filter module determines filtering for being filtered according to the Best Times length of window to the power signal sequence
Power signal sequence afterwards.
8. filtering system according to claim 7, which is characterized in that the time window length determination modul specifically wraps
It includes:
Dominant frequency determination unit, for determining dominant frequency according to the power signal sequence；
Sample frequency acquiring unit, for obtaining sample frequency；
Time window foundation length calculates unit, for calculating time window basis according to the dominant frequency and the sample frequency
Length；
Time window length determination unit, it is every in the power signal sequence for being determined according to the time window foundation length
The time window length of one signal.
9. filtering system according to claim 7, which is characterized in that the parsing sequence construct module specifically includes:
Sequence construct unit is parsed, for according to formulaBuilding parsing sequence；Wherein, z (n) is constructed
Parse the value of the nth point of sequence；μ is the index of modulation, 1 μ≤2 <；I is imaginary unit,PjFor power signal sequence P
In j-th of signal.
10. filtering system according to claim 9, which is characterized in that the pseudo NM-algebra determining module
It specifically includes:
Pseudo NM-algebra determination unit, for according to formula
Determine pseudo NM-algebra of the parsing sequence under the current layer time window length；Wherein, PWz c(n,f)
For the pseudo NM-algebra under c-th of time window length；hcIt (m) is the time window under c-th of time window length
Function；z*It (n-m) is the conjugation of z (n-m), since z (n-m) is real number, z*(n-m)=z (n-m), z (n+m) are the parsing sequence
The value of n-th+m elements of column, e-i4πfm=cos (4 π fm)-isin (4 π fm), i is imaginary unit, and n is serial number, is positive integer；
M is the parameter in sum term, is integer；F is frequency.
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| CN111681667B (en) * | 2020-06-23 | 2021-05-04 | 青岛科技大学 | Underwater sound signal denoising method based on adaptive window filtering and wavelet threshold optimization |
| CN112327084A (en) * | 2020-11-03 | 2021-02-05 | 华北电力大学 | Method and system for detecting vibration and sound of running state of transformer by utilizing equidistant transformation |
| CN112327084B (en) * | 2020-11-03 | 2022-01-21 | 华北电力大学 | Method and system for detecting vibration and sound of running state of transformer by utilizing equidistant transformation |
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